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AstyanM

MCP Data Science

by AstyanM
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    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A3.5/5.0

    Scored across 102 tools

    Disambiguation3/5

    Most tools have distinct purposes, but several families overlap: group_aggregate, group_aggregate_multi, and describe_by_group; variance_filter, correlation_filter, and drop_low_importance; and multiple encoding tools. With 102 tools, an agent has a real risk of selecting the wrong one despite helpful descriptions.

    Naming Consistency4/5

    The overwhelming majority follow a clear verb_noun snake_case pattern (load_csv, drop_duplicates, train_model, plot_histogram). A few outliers like crosstab, predict, and group_aggregate_multi break the pattern slightly, but the convention is highly predictable overall.

    Tool Count1/5

    102 tools is far beyond any reasonable scope for a coherent toolset, even for a broad data science domain. This dramatically increases cognitive load and selection difficulty, making the server feel more like a sprawling library than a focused MCP tool surface.

    Completeness5/5

    The toolset covers the entire data science lifecycle: loading, cleaning, EDA, visualization, feature engineering, encoding, modeling, evaluation, clustering, statistical testing, and reporting. Minor gaps exist (e.g., no model deletion or explicit model persistence), but there are no meaningful dead ends for the intended workflows.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

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